Remove Clustering Remove Data Pipeline Remove Data Preparation Remove ML
article thumbnail

Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. A provisioned or serverless Amazon Redshift data warehouse.

article thumbnail

How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model

AWS Machine Learning Blog

Training an LLM is a compute-intensive and complex process, which is why Fastweb, as a first step in their AI journey, used AWS generative AI and machine learning (ML) services such as Amazon SageMaker HyperPod. The dataset was stored in an Amazon Simple Storage Service (Amazon S3) bucket, which served as a centralized data repository.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. SageMaker Studio is the first fully integrated development environment (IDE) for ML. Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster.

ML 123
article thumbnail

Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning Blog

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

article thumbnail

Deploying Gen AI in Production with NVIDIA NIM & MLRun

Iguazio

The blog is based on the webinar Deploying Gen AI in Production with NVIDIA NIM & MLRun with Amit Bleiweiss, Senior Data Scientist at NVIDIA, and Yaron Haviv, co-founder and CTO and Guy Lecker, ML Engineering Team Lead at Iguazio (acquired by McKinsey). Ensuring data security, lineage and risk controls.

AI 52
article thumbnail

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

article thumbnail

Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning Blog

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment.

AWS 123